567 lines
20 KiB
Python
567 lines
20 KiB
Python
|
||
import json
|
||
import os
|
||
import argparse
|
||
import random
|
||
|
||
# 科学类别文本常量
|
||
CATEGORY_TEXT = """ A. quant-ph
|
||
B. physics.chem-ph
|
||
C. physics.atom-ph
|
||
D. cond-mat.soft
|
||
E. cs.RO
|
||
F. cs.CL
|
||
G. cs.SE
|
||
H. cs.IR
|
||
I. hep-th
|
||
J. hep-ph
|
||
K. physics.optics
|
||
L. cs.AI
|
||
M. cs.CV
|
||
N. nucl-th
|
||
O. astro-ph
|
||
P. math.PR
|
||
Q. cs.OS
|
||
R. eess.SP
|
||
S. math.OC
|
||
T. math.DS
|
||
U. math.DG
|
||
V. math.MP
|
||
W. cs.MM
|
||
X. stat.ME
|
||
Y. math.CO
|
||
Z. cs.NE
|
||
"""
|
||
# 科学类别字典
|
||
CATEGORY_DICT = {
|
||
"quant-ph": "A",
|
||
"physics.chem-ph": "B",
|
||
"physics.atom-ph": "C",
|
||
"cond-mat.soft": "D",
|
||
"cs.RO": "E",
|
||
"cs.CL": "F",
|
||
"cs.SE": "G",
|
||
"cs.IR": "H",
|
||
"hep-th": "I",
|
||
"hep-ph": "J",
|
||
"physics.optics": "K",
|
||
"cs.AI": "L",
|
||
"cs.CV": "M",
|
||
"nucl-th": "N",
|
||
"astro-ph": "O",
|
||
"math.PR": "P",
|
||
"cs.OS": "Q",
|
||
"eess.SP": "R",
|
||
"math.OC": "S",
|
||
"math.DS": "T",
|
||
"math.DG": "U",
|
||
"math.MP": "V",
|
||
"cs.MM": "W",
|
||
"stat.ME": "X",
|
||
"math.CO": "Y",
|
||
"cs.NE": "Z"
|
||
}
|
||
# 问题模板常量
|
||
QUESTION_TEMPLATES = [
|
||
# 直接提问式
|
||
"{category_text}What is the scientific category for a paper titled '{title}', authored by {authors}, with abstract '{abstract}'?",
|
||
|
||
# 命令式
|
||
"Classify this paper into its scientific category based on title '{title}', authors '{authors}', and abstract '{abstract}'.{category_text}",
|
||
|
||
# 描述性引导
|
||
"{category_text}Given a research paper with title '{title}', authors {authors}, and abstract '{abstract}', identify the appropriate discipline.",
|
||
|
||
# 正式请求
|
||
"Please assign the scientific category for the paper: title '{title}', authors '{authors}', abstract '{abstract}'.{category_text}",
|
||
|
||
# 摘要优先
|
||
"Using the abstract '{abstract}', title '{title}', and authors '{authors}', determine the paper's category.{category_text}",
|
||
|
||
# 作者强调
|
||
"{category_text}From authors '{authors}', title '{title}', and abstract '{abstract}', what category does this paper fall into?",
|
||
|
||
# 问题链式
|
||
"Here's a paper: title '{title}', authors {authors}, abstract '{abstract}'. What is its scientific category?{category_text}",
|
||
|
||
# 简洁版
|
||
"Category for: title '{title}', authors '{authors}', abstract '{abstract}'?{category_text}",
|
||
|
||
# 上下文嵌入
|
||
"Considering the title '{title}', the authors '{authors}', and the abstract content '{abstract}', please specify the paper's field.{category_text}",
|
||
|
||
# 非正式口语
|
||
"Hey, what category is this paper? Title '{title}', by {authors}, abstract '{abstract}'.{category_text}",
|
||
|
||
# 元素罗列
|
||
"{category_text}Title: '{title}'. Authors: '{authors}'. Abstract: '{abstract}'. Now, what's the scientific category?",
|
||
|
||
# 假设场景
|
||
"If a paper has title '{title}', authors '{authors}', and abstract '{abstract}', which scientific category best fits it?{category_text}",
|
||
|
||
# 强调关键信息
|
||
"Based solely on the title '{title}', authors list '{authors}', and abstract text '{abstract}', categorize this paper.{category_text}",
|
||
|
||
# 间接询问
|
||
"For the paper '{title}' by {authors}, with abstract '{abstract}', could you indicate its scientific discipline?{category_text}",
|
||
|
||
# 完整句子整合
|
||
"Determine the category of the research paper entitled '{title}', written by {authors}, and summarized as '{abstract}'.{category_text}",
|
||
|
||
# 问题聚焦摘要
|
||
"The abstract '{abstract}' describes a paper titled '{title}' by authors '{authors}'. What category is it?{category_text}",
|
||
|
||
# 标题驱动
|
||
"{category_text}Starting from the title '{title}', and considering authors '{authors}' and abstract '{abstract}', what is the paper's category?",
|
||
|
||
# 多部分查询
|
||
"Part 1: Title is '{title}'. Part 2: Authors are '{authors}'. Part 3: Abstract is '{abstract}'. Based on this, classify the paper.{category_text}",
|
||
|
||
# 比较式
|
||
"Given the details: title '{title}', authors '{authors}', abstract '{abstract}', how would you categorize this paper scientifically?{category_text}",
|
||
|
||
# 行动导向
|
||
"Using the provided title '{title}', authors '{authors}', and abstract '{abstract}', output the scientific category for this paper.{category_text}"
|
||
]
|
||
|
||
QUESTION_TEMPLATES = [
|
||
"Based on the title '{title}', authors '{authors}', and abstract '{abstract}', please determine the scientific category of this paper.\n\n{category_text}"
|
||
]
|
||
|
||
|
||
|
||
def extract_title_author_and_abstract(content_text):
|
||
"""
|
||
content_text: 格式示例"Based on the title 'The Quantum Primordial Black Holes, Dimensionless Small Parameter, Inflationary Cosmology and Non-Gaussianity', authors 'Alexander Shalyt-Margolin', and abstract 'In the present work consideration is given to the primordial black holes ({\\bf pbhs}) in the Schwarzschild-de Sitter Metric with small mass (ultralight) in the preinflationary epoch. Within the scope of natural assumptions, it has been shown that the quantum-gravitational corrections ({\\bf qgcs}) to the characteristics of such black holes can contribute to all the cosmological parameters, shifting them compared with the semiclassical consideration. These contributions are determined by a series expansion in terms of a small parameter dependent on the hole mass (radius). For this pattern different cases have been considered (stationary, black hole evaporation...). It has been demonstrated that involvement of ({\\bf qgcs}) leads to a higher probability for the occurrence of such {\\bf pbhs}. Besides, high-energy deformations of Friedmann Equations created on the basis of these corrections have been derived for different patterns. In the last section of this work it is introduced a study into the contributions generated by the above-mentioned {\\bf qgcs} in inflationary cosmological perturbations. Besides, it has been shown that non-Gaussianity of these perturbations is higher as compared to the semi-classical pattern.', please determine the scientific category of this paper. Additional info: 35 pages, Latex ,
|
||
A. quant-ph\nB. physics.chem-ph\nC. physics.atom-ph\nD. cond-mat.soft\nE. cs.RO\nF. cs.CL\nG. cs.SE\nH. cs.IR\nI. hep-th\nJ. hep-ph\nK. physics.optics\nL. cs.AI\nM. cs.CV\nN. nucl-th\nO. astro-ph\nP. math.PR\nQ. cs.OS\nR. eess.SP\nS. math.OC\nT. math.DS\nU. math.DG\nV. math.MP\nW. cs.MM\nX. stat.ME\nY. math.CO\nZ. cs.NE", "assistant": "I"}]}}
|
||
|
||
|
||
"""
|
||
try:
|
||
# 针对可以直接解析的JSON格式数据进行处理
|
||
if content_text.strip().startswith('{') and '"title"' in content_text and ('"author_names"' in content_text or '"authors"' in content_text):
|
||
try:
|
||
# 尝试解析为JSON对象
|
||
paper_data = json.loads(content_text)
|
||
title = paper_data.get("title", "")
|
||
authors = ", ".join(paper_data.get("author_names", paper_data.get("authors", [])))
|
||
abstract = paper_data.get("summary", paper_data.get("abstract", ""))
|
||
return {"title": title, "authors": authors, "abstract": abstract}
|
||
except:
|
||
pass
|
||
|
||
#content_text.split("',")
|
||
parts = content_text.split("',")
|
||
if len(parts) < 3:
|
||
# 如果分割后的部分少于3个，返回默认值
|
||
return {"title": "", "authors": "", "abstract": ""}
|
||
|
||
# 安全地提取标题
|
||
title_parts = parts[0].split("'")
|
||
if len(title_parts) >= 2:
|
||
title = title_parts[1].strip()
|
||
else:
|
||
title = ""
|
||
|
||
# 安全地提取作者
|
||
authors_parts = parts[1].split("'")
|
||
if len(authors_parts) >= 2:
|
||
authors = authors_parts[1].strip()
|
||
else:
|
||
authors = ""
|
||
|
||
# 安全地提取摘要
|
||
abstract_parts = parts[2].split("'")
|
||
if len(abstract_parts) >= 2:
|
||
abstract = abstract_parts[1].strip()
|
||
else:
|
||
abstract = ""
|
||
|
||
return {"title": title, "authors": authors, "abstract": abstract}
|
||
except Exception as e:
|
||
# 如果出现任何异常，返回默认值
|
||
print(f"解析内容时出错: {e}")
|
||
return {"title": "", "authors": "", "abstract": ""}
|
||
|
||
def parse_new_format_data(data):
|
||
"""
|
||
解析新格式的数据
|
||
|
||
Args:
|
||
data: 新格式的JSON数据
|
||
|
||
Returns:
|
||
tuple: (system_instruction, human_content, assistant_content) 或 (None, None, None)
|
||
"""
|
||
if "messages" not in data or not isinstance(data["messages"], list) or len(data["messages"]) < 3:
|
||
return None, None, None
|
||
|
||
system_instruction = ""
|
||
human_content = ""
|
||
assistant_content = ""
|
||
|
||
for msg in data["messages"]:
|
||
if msg["role"] == "system":
|
||
system_instruction = msg["content"]
|
||
elif msg["role"] == "user":
|
||
human_content = msg["content"]
|
||
elif msg["role"] == "assistant":
|
||
assistant_content = msg["content"]
|
||
|
||
return system_instruction, human_content, assistant_content
|
||
|
||
|
||
def parse_old_format_data(data):
|
||
"""
|
||
解析旧格式的数据
|
||
|
||
Args:
|
||
data: 旧格式的JSON数据
|
||
|
||
Returns:
|
||
tuple: (system_instruction, conversation_data) 或 (None, None)
|
||
"""
|
||
if "system" not in data or "conversation" not in data or not data["conversation"]:
|
||
return None, None
|
||
|
||
system_instruction = data.get("system", "根据论文的标题、作者和摘要，确定该论文的科学类别。")
|
||
return system_instruction, data["conversation"]
|
||
|
||
|
||
def generate_multi_type_samples(title, authors, abstract, system_instruction, assistant_content, num_templates):
|
||
"""
|
||
根据模板生成多种类型的样本
|
||
|
||
Args:
|
||
title: 论文标题
|
||
authors: 作者
|
||
abstract: 摘要
|
||
system_instruction: 系统指令
|
||
assistant_content: 助手回复
|
||
num_templates: 使用的模板数量
|
||
|
||
Returns:
|
||
list: 生成的多种类型数据列表
|
||
"""
|
||
n = min(num_templates, len(QUESTION_TEMPLATES))
|
||
selected_templates = random.sample(QUESTION_TEMPLATES, n)
|
||
samples = []
|
||
|
||
for template in selected_templates:
|
||
formatted_question = template.format(
|
||
title=title,
|
||
authors=authors,
|
||
abstract=abstract,
|
||
category_text=CATEGORY_TEXT
|
||
)
|
||
|
||
new_data = {
|
||
"messages": [
|
||
{"role": "system", "content": system_instruction},
|
||
{"role": "user", "content": formatted_question},
|
||
{"role": "assistant", "content": assistant_content}
|
||
]
|
||
}
|
||
samples.append(new_data)
|
||
|
||
return samples
|
||
|
||
|
||
def process_new_format_data(data, num_templates):
|
||
"""
|
||
处理新格式数据
|
||
|
||
Args:
|
||
data: 新格式数据
|
||
num_templates: 模板数量
|
||
|
||
Returns:
|
||
list: 处理后的数据列表
|
||
"""
|
||
system_instruction, human_content, assistant_content = parse_new_format_data(data)
|
||
|
||
if not human_content:
|
||
return []
|
||
|
||
extracted = extract_title_author_and_abstract(human_content)
|
||
title = extracted.get("title", "")
|
||
authors = extracted.get("authors", "")
|
||
abstract = extracted.get("abstract", "")
|
||
|
||
return generate_multi_type_samples(title, authors, abstract, system_instruction, assistant_content, num_templates)
|
||
|
||
|
||
def process_old_format_data(data, num_templates):
|
||
"""
|
||
处理旧格式数据
|
||
|
||
Args:
|
||
data: 旧格式数据
|
||
num_templates: 模板数量
|
||
|
||
Returns:
|
||
list: 处理后的数据列表
|
||
"""
|
||
system_instruction, conversation_data = parse_old_format_data(data)
|
||
|
||
if not conversation_data:
|
||
return []
|
||
|
||
samples = []
|
||
for turn in conversation_data:
|
||
if "human" not in turn or "assistant" not in turn:
|
||
continue
|
||
|
||
extracted = extract_title_author_and_abstract(turn["human"])
|
||
title = extracted.get("title", "")
|
||
authors = extracted.get("authors", "")
|
||
abstract = extracted.get("abstract", "")
|
||
|
||
n = min(num_templates, len(QUESTION_TEMPLATES))
|
||
selected_templates = random.sample(QUESTION_TEMPLATES, n)
|
||
|
||
for template in selected_templates:
|
||
formatted_question = template.format(
|
||
title=title,
|
||
authors=authors,
|
||
abstract=abstract,
|
||
category_text=CATEGORY_TEXT
|
||
)
|
||
|
||
new_data = {
|
||
"system": system_instruction,
|
||
"conversation": [
|
||
{
|
||
"human": formatted_question,
|
||
"assistant": turn["assistant"]
|
||
}
|
||
]
|
||
}
|
||
samples.append(new_data)
|
||
|
||
return samples
|
||
|
||
|
||
def get_paper_data_from_crawl_jason(input_path):
|
||
"""
|
||
从指定文件夹里的所有JSON文件中获取论文数据
|
||
或从单个JSON文件中获取论文数据
|
||
"""
|
||
paper_data_list = []
|
||
|
||
# 检查输入路径是文件还是文件夹
|
||
if os.path.isfile(input_path):
|
||
# 如果是单个文件
|
||
paper_data_list.extend(_extract_paper_data_from_file(input_path))
|
||
print(f"从文件 {input_path} 中提取了 {len(paper_data_list)} 条数据")
|
||
elif os.path.isdir(input_path):
|
||
# 如果是文件夹，遍历其中所有JSON文件
|
||
files_found = 0
|
||
for filename in os.listdir(input_path):
|
||
if filename.endswith('.jsonl') :
|
||
file_path = os.path.join(input_path, filename)
|
||
try:
|
||
file_data = _extract_paper_data_from_file(file_path)
|
||
paper_data_list.extend(file_data)
|
||
print(f"已从 {filename} 中提取 {len(file_data)} 条数据")
|
||
files_found += 1
|
||
except Exception as e:
|
||
print(f"处理文件 {filename} 时出错: {e}")
|
||
print(f"在目录中找到 {files_found} 个JSON文件")
|
||
else:
|
||
print(f"路径 {input_path} 既不是文件也不是文件夹")
|
||
|
||
print(f"总共提取了 {len(paper_data_list)} 条论文数据")
|
||
return paper_data_list
|
||
|
||
def _extract_paper_data_from_file(file_path):
|
||
"""
|
||
从单个JSON文件中提取论文数据
|
||
|
||
Args:
|
||
file_path: JSON文件路径
|
||
|
||
Returns:
|
||
list: 论文数据列表
|
||
"""
|
||
paper_data_list = []
|
||
|
||
# 处理JSONL格式文件
|
||
with open(file_path, "r", encoding="utf-8") as f:
|
||
for line_num, line in enumerate(f, 1):
|
||
line = line.strip()
|
||
if not line: # 跳过空行
|
||
continue
|
||
try:
|
||
item = json.loads(line)
|
||
title = item.get("title", "")
|
||
# 处理作者信息的不同可能格式
|
||
authors_list = item.get("author_names", item.get("authors", []))
|
||
if isinstance(authors_list, list):
|
||
authors = ", ".join(authors_list)
|
||
else:
|
||
authors = str(authors_list)
|
||
|
||
# 处理摘要信息的不同可能格式
|
||
abstract = item.get("summary", item.get("abstract", ""))
|
||
# 处理分类信息的不同可能格式
|
||
category = item.get("category", "Unknown")
|
||
# 如果没有category字段，尝试从categories列表中获取第一个
|
||
if category == "Unknown" and "categories" in item and isinstance(item["categories"], list) and len(item["categories"]) > 0:
|
||
category = item["categories"][0]
|
||
|
||
# 提取论文数据
|
||
paper_data_dict = {
|
||
"title": title,
|
||
"authors": authors,
|
||
"abstract": abstract,
|
||
"category": category
|
||
}
|
||
paper_data_list.append(paper_data_dict)
|
||
except json.JSONDecodeError as e:
|
||
print(f"解析文件 {file_path} 的第 {line_num} 行时出错: {e}")
|
||
continue
|
||
|
||
return paper_data_list
|
||
|
||
|
||
|
||
def convert_onedata2multi_type_pre(paper_datas, output_file, num_templates):
|
||
"""
|
||
读取input_file，将Swift格式的1条数据按多种问题模板格式转换为多条数据，
|
||
并保存为output_file
|
||
|
||
参数:
|
||
input_file: 输入文件路径
|
||
output_file: 输出文件路径
|
||
num_templates: 每条数据生成的模板数量
|
||
"""
|
||
print(f"开始转换数据...每条数据生成{num_templates}条变体")
|
||
print(f"开始转换数据: {input_file} -> {output_file}")
|
||
|
||
multi_type_data = []
|
||
|
||
|
||
for item in paper_datas:
|
||
title = item.get("title", "")
|
||
authors = item.get("authors", "")
|
||
abstract = item.get("summary", item.get("abstract", ""))
|
||
|
||
|
||
|
||
n = min(num_templates, len(QUESTION_TEMPLATES))
|
||
selected_templates = random.sample(QUESTION_TEMPLATES, n)
|
||
|
||
for template in selected_templates:
|
||
formatted_question = template.format(
|
||
title=title,
|
||
authors=authors,
|
||
abstract=abstract,
|
||
category_text=CATEGORY_TEXT
|
||
)
|
||
|
||
|
||
new_data = {
|
||
|
||
"messages": [
|
||
{
|
||
"role": "assistant",
|
||
"content": formatted_question
|
||
#"assistant": row["answer"]
|
||
}
|
||
]
|
||
}
|
||
multi_type_data.append(new_data)
|
||
|
||
|
||
# 写入输出文件
|
||
with open(output_file, "w", encoding="utf-8") as f:
|
||
for item in multi_type_data:
|
||
f.write(json.dumps(item, ensure_ascii=False) + "\n")
|
||
|
||
print(f"转换完成! 共转换 {len(multi_type_data)} 条数据")
|
||
|
||
|
||
|
||
|
||
def convert_onedata2multi_type_sft(paper_datas, output_file, num_templates):
|
||
"""
|
||
读取input_file，将Swift格式的1条数据按多种问题模板格式转换为多条数据，
|
||
并保存为output_file
|
||
|
||
参数:
|
||
input_file: 输入文件路径
|
||
output_file: 输出文件路径
|
||
num_templates: 每条数据生成的模板数量
|
||
"""
|
||
print(f"开始转换数据...每条数据生成{num_templates}条变体")
|
||
print(f"开始转换数据: {input_file} -> {output_file}")
|
||
|
||
multi_type_data = []
|
||
|
||
|
||
for item in paper_datas:
|
||
title = item.get("title", "")
|
||
authors = item.get("authors", "")
|
||
abstract = item.get("summary", item.get("abstract", ""))
|
||
category = item.get("category", "Unknown")
|
||
answer=CATEGORY_DICT.get(category, "Unknown")
|
||
#print(item)
|
||
# 生成系统指令
|
||
system_instruction = "你是个优秀的论文分类师,根据论文的标题、作者和摘要，确定该论文的科学类别。"
|
||
|
||
n = min(num_templates, len(QUESTION_TEMPLATES))
|
||
selected_templates = random.sample(QUESTION_TEMPLATES, n)
|
||
|
||
for template in selected_templates:
|
||
formatted_question = template.format(
|
||
title=title,
|
||
authors=authors,
|
||
abstract=abstract,
|
||
category_text=CATEGORY_TEXT
|
||
)
|
||
|
||
new_data = {
|
||
"system": system_instruction,
|
||
"conversation": [
|
||
{
|
||
"human": formatted_question,
|
||
"assistant": answer
|
||
}
|
||
]
|
||
}
|
||
multi_type_data.append(new_data)
|
||
|
||
|
||
# 写入输出文件
|
||
with open(output_file, "w", encoding="utf-8") as f:
|
||
for item in multi_type_data:
|
||
f.write(json.dumps(item, ensure_ascii=False) + "\n")
|
||
|
||
print(f"转换完成! 共转换 {len(multi_type_data)} 条数据")
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# 示例用法
|
||
input_file = r"G:\\11\data-prepare\\arxiv_papers\\"
|
||
output_file_sft = r"G:\\11\data-prepare\\arxiv_papers-multi_type-sft.json"
|
||
output_file_pre = r"G:\\11\data-prepare\\arxiv_papers-multi_type-pre.json"
|
||
paper_datas=get_paper_data_from_crawl_jason(input_file)
|
||
convert_onedata2multi_type_sft(paper_datas, output_file_sft, num_templates=1)
|
||
#convert_onedata2multi_type_pre(paper_datas, output_file_pre, num_templates=1)
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|